Why can't we combine both training and validation data, given that both types of data are used for developing the model?
Usually we are splinting the training dataset because we want to fine tune, find the best hyper-parameters, for our model. If we combine the validation and training dataset given a network complex enough we could achieve perfect performance for the given task. But having very good performance on the training dataset does not mean our model is useful. It might be the case that our model is very good on the training dataset, but it fails to generalize, so when given samples outside of the training dataset the performance will significantly drop (this is called over-fitting). By splitting the dataset, we are testing how well our model is performing on new/unseen data.